More than ever before, enterprises are charged with establishing accurate forecasts for enterprise operations. Failing to meet established expectations can have significant negative impact on the enterprise in the areas of cash flow, stock price, liquidity, and investor faith, among other areas. Examples of enterprise planning activities for which accuracy is critical include revenue forecasting, inventory management, resource planning, and the like. Enterprise business planning, however, is a difficult and expensive task that often produces inaccurate results.
Conventionally, businesses have taken either a “top-down” or a “bottom-up” approach to enterprise planning. In “top-down” planning, businesses identify fundamental business targets, such as average product price, cost per employee, and the like, and push the targets down through the hierarchical structure of the corporation. In contrast, “bottom-up” planning involves the aggregation of low-level forecasts from the lowest cost centers of an organization. For budget planning, for example, management personnel may be required to periodically forecast expenses, and allocate the expenses to a number of categories, such as advertisement, travel, and salaries. However, the bottom-up forecasts rarely, if ever, reconcile with top-down business targets.
This information has typically been collected using paper or, more recently, electronic forms, such as an electronic template created with a spreadsheet software program. This often leaves the financial department of the enterprise with the difficult task of consolidating uncoordinated plans that have been compiled using inconsistent assumptions and varying business logic.
More recently, large computer systems have been used to collect the data via an enterprise network. The computer systems typically consolidate data collected from the various enterprise users using time-consuming, offline batch processing during “off” hours. This offline consolidation can lead to significant time delays between the collection of the data from a user, and the consolidation of the collected data with other data collected from the enterprise. As a result, such systems often present users an inaccurate view of the actual, aggregated data for the enterprise activity being forecasted. This may lead the users to provide incorrect data, or erroneously modify their input. Furthermore, the users may be unsure as to which numbers are the “right” numbers for the enterprise, and may generally doubt the integrity of the results. This slow process of data collection and offline consolidation can be particularly problematic for a heavily deadline-oriented activity like enterprise planning.